Method of palmprint identification

ABSTRACT

A method of palmprint identification includes obtaining an image of a portion of an inner surface of a hand of an individual. A sub-image of skin surface within a defined area of the inner surface of the hand of obtained from the image. The sub-image is analyzed to obtain texture data for the skin surface with the defined area. The texture data is compared to reference information in a database.

BACKGROUND TO THE INVENTION

[0001] 1. Field of the Invention

[0002] The invention relates to biometrics identification, and inparticular to a method for analyzing a palmprint for the identificationof an individual.

[0003] 2. Background Information

[0004] Using palmprint recognition as a method for personalidentification is a new biometrics technology replacing fingerprints.Known methods include analyzing a palmprint to identify singular points,minutiae, and wrinkles in a palmprint image. These known methods requirea high-resolution image as illustrated in FIG. 1. This can be obtainedby way of inked palmprints. However, these are messy and cannot beobtained passively for real-time identification.

[0005] To overcome the problem of inked palmprints some companies havedeveloped high-resolution palmprint scanners and identification systems.However, these devices capturing high-resolution images are costly andrely on high performance computers to fulfil the requirements ofreal-time identification.

[0006] One solution to the above problems seems to be the use oflow-resolution images. FIG. 2 illustrates low-resolution imagescorresponding to FIG. 1. In low-resolution palmprint images, however,singular points and minutiae cannot be observed easily therefore moreeasily identifiable wrinkles must play an important role in theidentification. It is noted from FIG. 2 however, that only a smallproportion of wrinkles are significantly clear, but it is questionablewhether they provide sufficient distinctiveness to reliably identifyindividuals amongst a large population,

SUMMARY OF THE INVENTION

[0007] It is an object of the present invention to provide a method ofbiometrics identification, and in particular a method for analyzing apalmprint for the identification of an individual, which overcomes orameliorates the above problems.

[0008] According to a first aspect of the invention there is provided amethod of biometrics identification including:

[0009] obtaining an image of an area of skin surface from an individual,

[0010] analyzing the image to extract texture features on the area ofskin surface, and

[0011] comparing the texture features with reference information in adatabase.

[0012] According to a second aspect of the invention there is provided amethod of biometrics identification including:

[0013] obtaining an image of a portion of an inner surface of a hand ofan individual,

[0014] obtaining a sub-image of skin surface within a defined area ofthe inner surface of the hand,

[0015] analyzing the sub-image to obtain texture data for the skinsurface, and

[0016] comparing the texture data with reference information in adatabase.

[0017] Preferably, the defined area is dependent on one or morecharacteristics of the hand.

[0018] Preferably, the one or more characteristics are the areas betweenfingers of the hand.

[0019] Preferably, the sub-image is obtained by steps including:

[0020] identifying at least two points representing the areas betweenfingers of the hand,

[0021] determining a coordinate system having a first and a second axis,wherein the two points are located on the first axis and are equidistantfrom the second axis, and

[0022] determining parameters of the sub-image within the coordinatesystem using the distance between the two points.

[0023] Preferably, the parameters of the sub-image include points in thecoordinate system represented by:

[0024] (0.25D, 0.5D), (1.25D, 0.5D), (0.25D, −0.5D) and (1.25D, −0.5D)

[0025] where D is the distance between the two points.

[0026] Preferably, there is a further step of normalizing the sub-image.

[0027] Preferably, analyzing the sub-image includes using a GaborFilter.

[0028] Preferably, analyzing the sub-image includes segmenting layers ofthe sub-image with low resolution using Gabor analysis.

[0029] Preferably, the sub-image is segmented into two parts, a realpart and an imaginary part, each part being stored as a vector.

[0030] Preferably, comparing the texture data with reference informationin the database is based on a hamming distance of the form:${D_{0} = \frac{{\sum\limits_{i = 1}^{N}{\sum\limits_{j \neq 1}^{N}{P_{M}\left( {i,j} \right)}}}\bigcap{{Q_{M}\left( {i,j} \right)}\left( \left( {{{P_{R}\left( {i,j} \right)} \otimes {Q_{R}\left( {i,j} \right)}} + {{P_{i}\left( {i,j} \right)} \otimes {Q_{i}\left( {i,j} \right)}}} \right) \right)}}{{2{\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}{P_{M}\left( {i,j} \right)}}}}\bigcap{Q_{M}\left( {i,j} \right)}}},$

[0031] where P_(R) (Q_(R)) and P_(I) (Q_(I)) are the real part and theimaginary part.

[0032] Further aspects of the invention will become apparent from thefollowing description, which is given by way of example only.

BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Embodiments of the invention will now be described with referenceto the accompanying drawings in which:

[0034]FIG. 1 illustrates typical high-resolution palmprints images,

[0035]FIG. 2 illustrates typical low-resolution palmprints images,

[0036] FIGS. 3 to 8 illustrate preprocessing of an image of the insideof a hand,

[0037]FIGS. 9 and 10 illustrate incorrect placement of a hand on a palmreader and the corresponding preprocessed image,

[0038] FIGS. 11 to 14 illustrate the preprocessed image, real andimaginary parts and the masks.

[0039]FIGS. 15 and 16 illustrate the difference in image quality betweenfirst and second collected images,

[0040]FIGS. 17 and 18 show verification test results for a methodaccording to the invention, and

DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0041] A method of palmprint identification according to the inventioncomprises three parts: 1) obtaining an image of the palmprint of anindividual, 2) analyzing the skin texture data from the image and 3)matching the skin texture data with information stored in a database.These steps are described in more detail below.

[0042] 1) Obtaining an Image of the Palmprint of an Individual

[0043] Referring to FIG. 3, a low-resolution image of a portion theinside surface of a hand is obtained in known manner using a CCD camera.In order to extract identification data from the image a repeatablesub-image of the palm area must be identified using characteristics ofthe hand. In the preferred embodiment the holes between fingers areidentified and used as the parameters to build a coordinate system inwhich parameters that define the sub-image can be found. The preferredembodiment has six main steps, which are given below.

[0044] Referring to FIG. 4, the first step is to apply a lowpass filter,L(u,v), such as Gaussian, to the original is image, Q(x,y). Then, athreshold, T_(p), is used to convert the convoluted image to a binaryimage, B(x,y).

[0045] Referring to FIG. 5 the second step is to obtain the boundariesof the holes, (F_(i)x_(j), F_(i)y_(j)): where i=1, 2, between thefingers using a boundary tracking algorithm. The boundary of the holebetween ring and middle fingers is not extracted since it is not usefulfor the following processing.

[0046] Referring to FIG. 6, the third step is to compute the tangent ofthe holes (F_(i)x_(j), F_(i)y_(j)). If (x₁, y₁) and (x₂, y₂) are twopoints on (F₁x_(j), F₁y_(j)) and (F₂x_(j), F₂y_(j)), respectively theline (y=mx+c) passing through these two points satisfies the inequality,F₁y_(j), mF₁x_(j)+C, for all i and j. The line (y=mx+c) is the tangentof the two holes. This line, represented by numeral 2 in FIG. 6, is theY-axis of the coordinate system for determining the location of thesub-image 1.

[0047] The fourth step is to find a line 3 passing through the midpointof the two points that is perpendicular to line 2 to determine theX-axis and origin of the coordinate system. The two points lie on theY-axis, equidistant from the X-axis.

[0048] The fifth step is to extract a sub-image 1 with a dynamic size onthe basis of the coordinate system. The size and location of thesub-image 1 are based on the Euclidean distance (D) between the twopoints (x₁, y₁) and (x₂, y₂). The points 4, 5, 6, 7 representing thecorners of the sub-image 1 in the coordinate system are (0.25D, 0.5D),(1.25D, 0.5D), (0.25D, −0.5D) and (1.25D, −0.5D) respectively. Thus thesub-image 1 is square with a distance along each side equal to theEuclidean distance and symmetrical about the Y-axis line 3. Because thesub-image is based on feature of the hand (the area between the fingers)it is repeatable for each individual hand.

[0049]FIG. 7 shows the x and y axes 2, 3 of the coordinate system andthe sub-image 1 overlaid on the raw image of FIG. 3.

[0050] The sixth step is to extract and normalize the sub-image 1 to astandard size using bilinear interpolation for feature extraction. FIG.8 shows the extracted and normalized sub-image 1.

[0051] Once the palm sub-image 1 is obtained the next part of the methodis undertaken.

[0052] 2) Analyzing the Skin Texture of the Image

[0053] The circular Gabor filter is an effective tool for textureanalysis, and has the following general form, $\begin{matrix}{{{G\left( {x,y,\theta,u,\sigma} \right)} = {\frac{1}{2\pi \quad \sigma^{2}}\exp \left\{ {- \frac{x^{2} + y^{2}}{2\sigma^{2}}} \right\} {\exp\left( {2\pi \quad {i\left( {{{ux}\quad \cos \quad \theta} + {u\quad y\quad \sin \quad \theta}} \right)}} \right\}}}},} & (1)\end{matrix}$

[0054] where i={square root}{square root over (−1)}; μ is the frequencyof the sinusoidal wave; θ controls the orientation of the function and σis the standard deviation of the Gaussian envelope. Gabor filters arewidely used in texture analysis and thus the skilled addressee will befamiliar with their use for such purpose,

[0055] In order to make the texture analysis more robust to variationsin image brightness a discrete Gabor filter G[x,y,θ,μ,σ] is turned tozero DC with the application of the following formula: $\begin{matrix}{{{\overset{\sim}{G}\left\lbrack {x,y,\theta,u,\sigma} \right\rbrack} = {{G\left\lbrack {x,y,\theta,u,\sigma} \right\rbrack} - \frac{\sum\limits_{j = {- n}}^{n}{\sum\limits_{j = {- n}}^{n}{G\left\lbrack {i,j,\theta,u,\sigma} \right\rbrack}}}{\left( {{2n} + 1} \right)^{2}}}},} & (2)\end{matrix}$

[0056] where (2n+1)² is the size of the filter. In fact, the imaginarypart of the Gabor filter automatically has zero DC because of oddsymmetry. The use of the adjusted Gabor filter is to filter thepreprocessed images. Then, the phase information is coded by thefollowing inequalities, $\begin{matrix}{{b_{r} = {{1\quad {if}\quad {{Re}\left( {\sum\limits_{y = {- n}}^{n}{\sum\limits_{x = {- n}}^{n}{{\overset{\sim}{G}\left\lbrack {x,y,\theta,u,\sigma} \right\rbrack}{I\left( {{x + x_{o}},{y + y_{o}}} \right)}}}} \right)}} \geq 0}},} & (3) \\{{b_{r} = {{0\quad {if}\quad {{Re}\left( {\sum\limits_{y = {- n}}^{n}{\sum\limits_{x = {- n}}^{n}{{\overset{\sim}{G}\left\lbrack {x,y,\theta,u,\sigma} \right\rbrack}{I\left( {{x + x_{o}},{y + y_{o}}} \right)}}}} \right)}} < 0}},} & (4) \\{{b_{l} = {{1\quad {if}\quad {{Im}\left( {\sum\limits_{y = {- n}}^{n}{\sum\limits_{x = {- n}}^{n}{{\overset{\sim}{G}\left\lbrack {x,y,\theta,u,\sigma} \right\rbrack}{I\left( {{x + x_{o}},{y + y_{o}}} \right)}}}} \right)}} \geq 0}},} & (5) \\{{b_{l} = {{0\quad {if}\quad {{Im}\left( {\sum\limits_{y = {- n}}^{n}{\sum\limits_{x = {- n}}^{n}{{\overset{\sim}{G}\left\lbrack {x,y,\theta,u,\sigma} \right\rbrack}{I\left( {{x + x_{o}},{y + y_{o}}} \right)}}}} \right)}} < 0}},} & (6)\end{matrix}$

[0057] where I(x, y) is a preprocessed image and (x₀, y₀) is center offiltering.

[0058] Referring to FIGS. 9 and 10, since it is expected that some userswill not place their hand correctly some non-palmprint pixels will becontain in the palm sub-image. A mask is generated to point out thelocation of the non-palmprint pixels Because the image source can beconsidered a semi-closed environment, the non-palmprint pixels come fromthe black boundaries of the image background. Thus a threshold can beused to segment the non-palmprint pixels. Typically, feature sizeincluding mask and palmprint features is 384 bytes.

[0059]FIG. 11 depict the preprocessed images, 12 depict the real part ofthe corresponding texture features, 13 depict the imaginary part of thecorresponding texture features, and FIG. 14 depicts the correspondingmasks.

[0060] A useful discussion on the use of Gabor filters for textureanalysis can be found in the following two publications.

[0061] A. Jain and G. Healey, “A multiscale representation includingopponent color features for texture recognition”, IEEE Transactions onImage Processing, vol. 7, no. 1, pp. 124-128, 1998.

[0062] D. Dunn and W. E. Higgins, “Optimal Gabor filters for texturesegmentation,” IEEE Transactions on Image Processing, vol. 4, no. 4, pp.947-964, 1995.

[0063] 3) Palmprint Matching

[0064] The real and imaginary features are represented as vectors, whichare compared to vectors of stored palmprint data. Palmprint matching isbased on a normalized hamming distance. For example, let P and Q be twopalmprint feature matrixes. The normalized hamming distance can bedescribed as, $\begin{matrix}{{D_{o} = \frac{{\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}{P_{M}\left( {i,j} \right)}}}\bigcap{{Q_{M}\left( {i,j} \right)}\left( \left( {{{P_{R}\left( {i,j} \right)} \otimes {Q_{R}\left( {i,j} \right)}} + {{P_{I}\left( {i,j} \right)} \otimes {Q_{i}\left( {i,j} \right)}}} \right) \right)}}{{2{\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}{P_{M}\left( {i,j} \right)}}}}\bigcap{Q_{M}\left( {i,j} \right)}}},} & (7)\end{matrix}$

[0065] where P_(R) (Q_(R)), P_(I) (Q_(I)) and P_(M)(Q_(M)) are the realpart, the imaginary part and mask of P (Q), respectively; the result ofBoolean operator, “{circumflex over (x)}”, is equal to zero if and onlyif the two bits, P_(R(I)) (i,j), equal to Q_(R(I)) (i,j); ∩ representsan AND operator and the size of the feature matrixes is N×N. It is notedthat D₀ is between 1 and 0. For perfect matching, the matching score iszero. Because of imperfect preprocessing, the features need to bevertically and horizontally translated and then matched again. Then, therange of vertical and horizontal translation is −2 to 2. The minimum ofD₀'s obtained by translated matching is considered as the final matchingscore,

[0066] The following experimental results illustrate the effectivenessof a system according to the invention.

[0067] Palmprint images were collected from 154 subjects using apalmprint scanner. Approximately 65% of the subjects were male. The agedistribution of the subjects is shown in the following Table 1. Rangesof Ages Percentage 10-20  2% 21-30 80% 31-40 12% 41-50 3 51-60  2% 61-70 1%

[0068] Each subject provided two groups of images. Each group contained10 images for the left palm and 10 images for the right palm. Totally,each subject provided 40 images to create an image database containing6191 images from 308 different palms. The average time differencebetween the collection of the first and second groups of image from eachsubject was 57 days. The maximum and minimum time differences were 90and 4 days respectively. After finishing the first collection, the lightsource was changed and the focus adjusted on the CCD camera so as tosimulate image collection by two different palmprint s scanners. FIGS.15 and 16 show corresponding hand images captured in the first andsecond groups for one subject. The collected images were in two sizes,384×284 and 768×568. The larger images were resized to 384×284;consequently, the size of all the test images in the followingexperiments is 384×284 with 75 dpi resolution.

[0069] To obtain the verification accuracy of the palmprint system, eachpalmprint image was matched with all palmprint images in the database. Amatching was noted as is a correct matching of two palmprint images fromthe same palm of the same subject. The total number of comparisons was19,161,145. The number of correct matches was 59,176.

[0070] A probability distributions for genuine and imposter areestimated by the correct and incorrect matching, respectively, is shownin FIG. 17. FIG. 18 depicts the corresponding Receiver Operating Curve(ROC), being a plot of genuine acceptance rate against false acceptancerate for all possible operating points. From FIG. 18 it is estimatedthat a method according to the invention can operate at 96k genuineacceptance rate and 0.1% false acceptance rate; the correspondingthreshold is 0.35. This result is comparable with prior art palmprintapproaches and other hand-based biometrics technologies including handgeometry and fingerprint verification.

[0071] A method according to the invention utilizes low-resolutionimages and has low-computational cost. The verification accuracy isfound to be comparable with known high-performance methods usinghigh-resolution images.

[0072] The invention can be used for access control, ATM and varioussecurity systems.

[0073] Where in the foregoing description reference has been made tointegers or elements having known equivalents then such are included asif individually set forth herein.

[0074] Embodiments of the invention have been described, however it isunderstood that variations, improvements or modifications can take placewithout departure from the spirit of the invention or scope of theappended claims.

What is claimed is:
 1. A method of biometrics identification including:obtaining an image of an area of skin surface from an individual,analyzing the image to extract texture features on the area of skinsurface, and comparing the texture features with reference informationin a database.
 2. A method of biometrics identification including:obtaining an image of a portion of an inner surface of a hand of anindividual, obtaining a sub-image of skin surface within a defined areaof the inner surface of the hand, analyzing the sub-image to obtaintexture data for the skin surface, and comparing the texture data withreference information in a database.
 3. A method of claim 2 wherein thedefined area is dependent on one or more characteristics of the hand. 4.A method of claim 3 wherein the one or more characteristics are theareas between fingers of the hand.
 5. A method of claim 2 wherein thesub-image is obtained by steps including: identifying at least twopoints representing the areas between fingers of the hand, determining acoordinate system having a first and a second axis, wherein the twopoints are located on the first axis and are equidistant from the secondaxis, and determining parameters of the sub-image within the coordinatesystem using the distance between the two points.
 6. A method of claim 5wherein the parameters of the sub-image include points in the coordinatesystem is represented by: (0.25D, 0.5D), (1.25D, 0.5D), (0.25D, −0.5D)and (1.25D, −0.5D) where D is the distance between the two points.
 7. Amethod of claim 5 including a further step of normalizing the sub-image.8. A method of claim 2 wherein analyzing the sub-image includes using aGabor Filter.
 9. A method of claim 2 wherein analyzing the sub-imageincludes segmenting layers of the sub-image with low resolution usingGabor analysis.
 11. A method of claim 2 wherein the sub-image issegmented into two parts, a real part and an imaginary part, each partbeing stored as a vector.
 12. A method of claim 11 wherein comparing thetexture lo data with reference information in the database is based on ahamming distance of the form:${D_{o} = \frac{{\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}{P_{M}\left( {i,j} \right)}}}\bigcap{{Q_{M}\left( {i,j} \right)}\left( \left( {{{P_{R}\left( {i,j} \right)} \otimes {Q_{R}\left( {i,j} \right)}} + {{P_{I}\left( {i,j} \right)} \otimes {Q_{I}\left( {i,j} \right)}}} \right) \right)}}{{2{\sum\limits_{i = 1}^{N}{\sum\limits_{j = 1}^{N}{P_{M}\left( {i,j} \right)}}}}\bigcap{Q_{M}\left( {i,j} \right)}}},$

where P_(R) (Q_(R)) and P_(I) (Q_(I)) are the real part and theimaginary part.